Light field images often suffer from low spatial resolution as the sampling resources are limited.
Cheng et al.  [1]} and Jin et al.  [2]} proposed to apply deep learning on constructing super-resolution images from light field images to cover this situation.
Depth estimation could also be achieved by leveraging the abundant information from light field images [3]}, [4]}, [5]}, [6]}, [7]}, [8]}, [9]}, [10]}, [11]}.
Levoy et al.  [3]} proposed to use the epipolar plane images (EPIs), which contain both spatial and angular information, for depth estimation from light field images.
Heber et al.  [4]} proposed to learn the end-to-end mapping between the 4D light field and its corresponding 4D depth field accompanied with a high-order regularization refinement.
By following [4]}, they then designed an encoder-decoder architecture to extract the geometric information from light field images [5]}.
Some works [16]}, [17]} focus on providing light field datasets with ground truth depth or AiF images for further use.
